Overview

Dataset statistics

Number of variables23
Number of observations1.088
Missing cells162
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory204.0 KiB
Average record size in memory192.0 B

Variable types

Text2
DateTime1
Categorical4
Numeric13
Boolean3

Alerts

Abdominal Circumference (cm) is highly overall correlated with Waist-to-Height RatioHigh correlation
BMI is highly overall correlated with CVD Risk Score and 1 other fieldsHigh correlation
CVD Risk Score is highly overall correlated with BMIHigh correlation
Estimated LDL (mg/dL) is highly overall correlated with Total Cholesterol (mg/dL)High correlation
Total Cholesterol (mg/dL) is highly overall correlated with Estimated LDL (mg/dL)High correlation
Waist-to-Height Ratio is highly overall correlated with Abdominal Circumference (cm)High correlation
Weight (kg) is highly overall correlated with BMIHigh correlation
Total Cholesterol (mg/dL) has 42 (3.9%) missing valuesMissing
Systolic BP has 40 (3.7%) missing valuesMissing
Diastolic BP has 51 (4.7%) missing valuesMissing
Patient ID has unique valuesUnique

Reproduction

Analysis started2026-02-15 02:51:22.029576
Analysis finished2026-02-15 02:51:29.744709
Duration7.72 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Patient ID
Text

Unique 

Distinct1088
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:29.842232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8.704
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1.088 ?
Unique (%)100.0%

Sample

1st rowisDx5313
2nd rowLHCK2961
3rd rowdCDO1109
4th rowpnpE1080
5th rowMQyB2747
ValueCountFrequency (%)
isdx53131
 
0.1%
lhck29611
 
0.1%
dcdo11091
 
0.1%
pnpe10801
 
0.1%
mqyb27471
 
0.1%
dhdn89681
 
0.1%
vkql97001
 
0.1%
nktq66891
 
0.1%
smmi39561
 
0.1%
alyl91881
 
0.1%
Other values (1078)1078
99.1%
2026-02-14T21:51:29.977633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7456
 
5.2%
9450
 
5.2%
0446
 
5.1%
8442
 
5.1%
1438
 
5.0%
2437
 
5.0%
6430
 
4.9%
4424
 
4.9%
5416
 
4.8%
3413
 
4.7%
Other values (52)4352
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7456
 
5.2%
9450
 
5.2%
0446
 
5.1%
8442
 
5.1%
1438
 
5.0%
2437
 
5.0%
6430
 
4.9%
4424
 
4.9%
5416
 
4.8%
3413
 
4.7%
Other values (52)4352
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7456
 
5.2%
9450
 
5.2%
0446
 
5.1%
8442
 
5.1%
1438
 
5.0%
2437
 
5.0%
6430
 
4.9%
4424
 
4.9%
5416
 
4.8%
3413
 
4.7%
Other values (52)4352
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7456
 
5.2%
9450
 
5.2%
0446
 
5.1%
8442
 
5.1%
1438
 
5.0%
2437
 
5.0%
6430
 
4.9%
4424
 
4.9%
5416
 
4.8%
3413
 
4.7%
Other values (52)4352
50.0%
Distinct841
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
Minimum2020-01-02 00:00:00
Maximum2025-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-14T21:51:30.023978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:30.081930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
M
547 
F
541 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.088
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M547
50.3%
F541
49.7%

Length

2026-02-14T21:51:30.136219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T21:51:30.165225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m547
50.3%
f541
49.7%

Most occurring characters

ValueCountFrequency (%)
M547
50.3%
F541
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M547
50.3%
F541
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M547
50.3%
F541
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M547
50.3%
F541
49.7%

Age
Real number (ℝ)

Distinct59
Distinct (%)5.5%
Missing7
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean46.876049
Minimum6.42
Maximum89.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:30.200948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.42
5-th percentile30
Q137
median46
Q355
95-th percentile71
Maximum89.42
Range83
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.682757
Coefficient of variation (CV)0.27055942
Kurtosis-0.14183112
Mean46.876049
Median Absolute Deviation (MAD)9
Skewness0.47576787
Sum50673.009
Variance160.85231
MonotonicityNot monotonic
2026-02-14T21:51:30.248202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3236
 
3.3%
3036
 
3.3%
3736
 
3.3%
4936
 
3.3%
4834
 
3.1%
3134
 
3.1%
4333
 
3.0%
3333
 
3.0%
3932
 
2.9%
5531
 
2.8%
Other values (49)740
68.0%
ValueCountFrequency (%)
6.421
 
0.1%
6.991
 
0.1%
257
 
0.6%
268
 
0.7%
277
 
0.6%
286
 
0.6%
299
 
0.8%
3036
3.3%
3134
3.1%
3236
3.3%
ValueCountFrequency (%)
89.421
 
0.1%
88.4641
 
0.1%
85.7151
 
0.1%
7910
0.9%
783
 
0.3%
769
0.8%
7512
1.1%
744
 
0.4%
735
0.5%
728
0.7%

Weight (kg)
Real number (ℝ)

High correlation 

Distinct828
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.109234
Minimum13.261
Maximum158.523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:30.309292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.261
5-th percentile53.17
Q166.68925
median87.023
Q3105.5
95-th percentile117.265
Maximum158.523
Range145.262
Interquartile range (IQR)38.81075

Descriptive statistics

Standard deviation22.078782
Coefficient of variation (CV)0.25640435
Kurtosis-0.81473197
Mean86.109234
Median Absolute Deviation (MAD)19.57532
Skewness-0.07842616
Sum93686.847
Variance487.47263
MonotonicityNot monotonic
2026-02-14T21:51:30.377968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.26
 
0.6%
100.45
 
0.5%
116.54
 
0.4%
69.24
 
0.4%
110.54
 
0.4%
83.64
 
0.4%
99.54
 
0.4%
91.43
 
0.3%
111.93
 
0.3%
97.83
 
0.3%
Other values (818)1048
96.3%
ValueCountFrequency (%)
13.2611
0.1%
15.0361
0.1%
19.5781
0.1%
21.0381
0.1%
21.3161
0.1%
46.650936021
0.1%
50.11
0.1%
50.21
0.1%
50.3071
0.1%
50.3431
0.1%
ValueCountFrequency (%)
158.5231
0.1%
157.1641
0.1%
149.8771
0.1%
143.79382041
0.1%
132.82342471
0.1%
124.36650421
0.1%
121.2005391
0.1%
1201
0.1%
119.91
0.1%
119.81
0.1%

Height (m)
Real number (ℝ)

Distinct316
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7558365
Minimum1.1960621
Maximum2.4803974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:30.428396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1960621
5-th percentile1.5727
Q11.6695
median1.7571216
Q31.842
95-th percentile1.95
Maximum2.4803974
Range1.2843353
Interquartile range (IQR)0.1725

Descriptive statistics

Standard deviation0.12260575
Coefficient of variation (CV)0.06982754
Kurtosis1.7421719
Mean1.7558365
Median Absolute Deviation (MAD)0.08712161
Skewness0.28544484
Sum1910.3502
Variance0.015032169
MonotonicityNot monotonic
2026-02-14T21:51:30.481092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8133
 
3.0%
1.7632
 
2.9%
1.6331
 
2.8%
1.730
 
2.8%
1.6627
 
2.5%
1.7427
 
2.5%
1.7326
 
2.4%
1.8726
 
2.4%
1.8625
 
2.3%
1.8325
 
2.3%
Other values (306)806
74.1%
ValueCountFrequency (%)
1.1960621361
0.1%
1.381
0.1%
1.3881
0.1%
1.411
0.1%
1.4634121481
0.1%
1.4909979071
0.1%
1.51
0.1%
1.5031
0.1%
1.5051
0.1%
1.5062
0.2%
ValueCountFrequency (%)
2.4803974051
0.1%
2.3949410081
0.1%
2.1755780371
0.1%
2.1461
0.1%
2.1411
0.1%
2.1391
0.1%
2.1255445981
0.1%
2.1171
0.1%
2.111
0.1%
2.0797031351
0.1%

BMI
Real number (ℝ)

High correlation 

Distinct639
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.610232
Minimum5.184
Maximum53.028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:30.527297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.184
5-th percentile17.8
Q122.38825
median28.35
Q334.2
95-th percentile40.065
Maximum53.028
Range47.844
Interquartile range (IQR)11.81175

Descriptive statistics

Standard deviation7.4147619
Coefficient of variation (CV)0.25916469
Kurtosis-0.51192687
Mean28.610232
Median Absolute Deviation (MAD)5.916
Skewness0.20667557
Sum31127.933
Variance54.978694
MonotonicityNot monotonic
2026-02-14T21:51:30.577755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.58
 
0.7%
33.48
 
0.7%
206
 
0.6%
35.66
 
0.6%
18.46
 
0.6%
20.86
 
0.6%
33.86
 
0.6%
19.76
 
0.6%
31.26
 
0.6%
27.16
 
0.6%
Other values (629)1024
94.1%
ValueCountFrequency (%)
5.1841
0.1%
6.2351
0.1%
152
0.2%
15.11
0.1%
15.31
0.1%
15.42
0.2%
15.51
0.1%
15.61
0.1%
15.72
0.2%
15.82
0.2%
ValueCountFrequency (%)
53.0281
0.1%
52.741
0.1%
52.1921
0.1%
52.1361
0.1%
51.9841
0.1%
51.0221
0.1%
46.21
0.1%
46.11
0.1%
45.61
0.1%
44.81
0.1%

Abdominal Circumference (cm)
Real number (ℝ)

High correlation 

Distinct712
Distinct (%)65.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean92.024074
Minimum49.542
Maximum136.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:30.622812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.542
5-th percentile72.4127
Q180.5
median91.5
Q3102.6
95-th percentile112.4337
Maximum136.336
Range86.794
Interquartile range (IQR)22.1

Descriptive statistics

Standard deviation13.317242
Coefficient of variation (CV)0.14471477
Kurtosis-0.56469397
Mean92.024074
Median Absolute Deviation (MAD)11.1
Skewness0.23575198
Sum100030.17
Variance177.34894
MonotonicityNot monotonic
2026-02-14T21:51:30.671158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.36
 
0.6%
916
 
0.6%
86.96
 
0.6%
96.66
 
0.6%
99.16
 
0.6%
101.16
 
0.6%
78.15
 
0.5%
94.45
 
0.5%
75.15
 
0.5%
74.25
 
0.5%
Other values (702)1031
94.8%
ValueCountFrequency (%)
49.5421
0.1%
701
0.1%
70.051
0.1%
70.0911
0.1%
70.12
0.2%
70.1841
0.1%
70.21
0.1%
70.32
0.2%
70.41
0.1%
70.4111
0.1%
ValueCountFrequency (%)
136.3361
0.1%
136.3191
0.1%
134.2971
0.1%
133.8461
0.1%
133.7351
0.1%
133.0651
0.1%
132.8611
0.1%
119.9961
0.1%
119.8741
0.1%
119.7361
0.1%
Distinct950
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:30.765891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters6.528
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique825 ?
Unique (%)75.8%

Sample

1st row112/83
2nd row101/91
3rd row92/89
4th row121/68
5th row107/61
ValueCountFrequency (%)
103/963
 
0.3%
111/973
 
0.3%
126/863
 
0.3%
129/613
 
0.3%
127/843
 
0.3%
127/773
 
0.3%
143/673
 
0.3%
113/773
 
0.3%
142/663
 
0.3%
114/633
 
0.3%
Other values (940)1058
97.2%
2026-02-14T21:51:30.896799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11569
24.0%
/1088
16.7%
9584
 
8.9%
6506
 
7.8%
7496
 
7.6%
0445
 
6.8%
8432
 
6.6%
2387
 
5.9%
3378
 
5.8%
4362
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)6528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11569
24.0%
/1088
16.7%
9584
 
8.9%
6506
 
7.8%
7496
 
7.6%
0445
 
6.8%
8432
 
6.6%
2387
 
5.9%
3378
 
5.8%
4362
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11569
24.0%
/1088
16.7%
9584
 
8.9%
6506
 
7.8%
7496
 
7.6%
0445
 
6.8%
8432
 
6.6%
2387
 
5.9%
3378
 
5.8%
4362
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11569
24.0%
/1088
16.7%
9584
 
8.9%
6506
 
7.8%
7496
 
7.6%
0445
 
6.8%
8432
 
6.6%
2387
 
5.9%
3378
 
5.8%
4362
 
5.5%

Total Cholesterol (mg/dL)
Real number (ℝ)

High correlation  Missing 

Distinct208
Distinct (%)19.9%
Missing42
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean198.83214
Minimum-1.256
Maximum385.679
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size17.0 KiB
2026-02-14T21:51:30.942108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.256
5-th percentile109
Q1151
median197.5
Q3250
95-th percentile290
Maximum385.679
Range386.935
Interquartile range (IQR)99

Descriptive statistics

Standard deviation58.857041
Coefficient of variation (CV)0.29601372
Kurtosis-0.69095551
Mean198.83214
Median Absolute Deviation (MAD)49.5
Skewness-0.068416955
Sum207978.42
Variance3464.1513
MonotonicityNot monotonic
2026-02-14T21:51:30.991601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16011
 
1.0%
17910
 
0.9%
10910
 
0.9%
2969
 
0.8%
1939
 
0.8%
1509
 
0.8%
1479
 
0.8%
1669
 
0.8%
2959
 
0.8%
1929
 
0.8%
Other values (198)952
87.5%
(Missing)42
 
3.9%
ValueCountFrequency (%)
-1.2561
 
0.1%
1.8171
 
0.1%
8.4981
 
0.1%
16.0881
 
0.1%
19.9321
 
0.1%
21.6621
 
0.1%
1006
0.6%
1014
0.4%
1022
 
0.2%
1034
0.4%
ValueCountFrequency (%)
385.6791
 
0.1%
3004
0.4%
2994
0.4%
2984
0.4%
2974
0.4%
2969
0.8%
2959
0.8%
2945
0.5%
2935
0.5%
2922
 
0.2%

HDL (mg/dL)
Real number (ℝ)

Distinct69
Distinct (%)6.4%
Missing9
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean55.819306
Minimum0.008
Maximum110.315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.038820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile32
Q142
median56
Q369
95-th percentile80.1
Maximum110.315
Range110.307
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.479661
Coefficient of variation (CV)0.29523228
Kurtosis-0.55255064
Mean55.819306
Median Absolute Deviation (MAD)14
Skewness0.013935268
Sum60229.031
Variance271.57923
MonotonicityNot monotonic
2026-02-14T21:51:31.084660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3828
 
2.6%
4627
 
2.5%
7826
 
2.4%
6326
 
2.4%
4425
 
2.3%
3124
 
2.2%
5923
 
2.1%
7623
 
2.1%
6123
 
2.1%
6623
 
2.1%
Other values (59)831
76.4%
ValueCountFrequency (%)
0.0081
 
0.1%
0.6121
 
0.1%
1.2761
 
0.1%
6.2831
 
0.1%
6.8091
 
0.1%
7.5421
 
0.1%
3022
2.0%
3124
2.2%
3222
2.0%
3317
1.6%
ValueCountFrequency (%)
110.3151
 
0.1%
108.3041
 
0.1%
104.8821
 
0.1%
897
0.6%
883
 
0.3%
876
0.6%
864
0.4%
855
0.5%
845
0.5%
838
0.7%

Fasting Blood Sugar (mg/dL)
Real number (ℝ)

Distinct134
Distinct (%)12.4%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean116.84532
Minimum70
Maximum219.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.277880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile74
Q191
median114
Q3137
95-th percentile176
Maximum219.667
Range149.667
Interquartile range (IQR)46

Descriptive statistics

Standard deviation31.151504
Coefficient of variation (CV)0.26660463
Kurtosis-0.047923674
Mean116.84532
Median Absolute Deviation (MAD)23
Skewness0.61974153
Sum126543.49
Variance970.41623
MonotonicityNot monotonic
2026-02-14T21:51:31.328236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7019
 
1.7%
9618
 
1.7%
11518
 
1.7%
9017
 
1.6%
14817
 
1.6%
8917
 
1.6%
9216
 
1.5%
10716
 
1.5%
10316
 
1.5%
7516
 
1.5%
Other values (124)913
83.9%
ValueCountFrequency (%)
7019
1.7%
716
 
0.6%
7210
0.9%
7314
1.3%
7411
1.0%
7516
1.5%
7611
1.0%
7713
1.2%
7810
0.9%
7912
1.1%
ValueCountFrequency (%)
219.6671
 
0.1%
219.1351
 
0.1%
218.0191
 
0.1%
215.6141
 
0.1%
213.6851
 
0.1%
212.9841
 
0.1%
212.3821
 
0.1%
1983
0.3%
1971
 
0.1%
1962
0.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
565 
False
523 
ValueCountFrequency (%)
True565
51.9%
False523
48.1%
2026-02-14T21:51:31.362737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
557 
False
531 
ValueCountFrequency (%)
True557
51.2%
False531
48.8%
2026-02-14T21:51:31.380451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
High
380 
Moderate
356 
Low
352 

Length

Max length8
Median length4
Mean length4.9852941
Min length3

Characters and Unicode

Total characters5.424
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowModerate
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High380
34.9%
Moderate356
32.7%
Low352
32.4%

Length

2026-02-14T21:51:31.412996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T21:51:31.439979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high380
34.9%
moderate356
32.7%
low352
32.4%

Most occurring characters

ValueCountFrequency (%)
e712
13.1%
o708
13.1%
g380
 
7.0%
i380
 
7.0%
H380
 
7.0%
h380
 
7.0%
M356
 
6.6%
d356
 
6.6%
r356
 
6.6%
a356
 
6.6%
Other values (3)1060
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)5424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e712
13.1%
o708
13.1%
g380
 
7.0%
i380
 
7.0%
H380
 
7.0%
h380
 
7.0%
M356
 
6.6%
d356
 
6.6%
r356
 
6.6%
a356
 
6.6%
Other values (3)1060
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e712
13.1%
o708
13.1%
g380
 
7.0%
i380
 
7.0%
H380
 
7.0%
h380
 
7.0%
M356
 
6.6%
d356
 
6.6%
r356
 
6.6%
a356
 
6.6%
Other values (3)1060
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e712
13.1%
o708
13.1%
g380
 
7.0%
i380
 
7.0%
H380
 
7.0%
h380
 
7.0%
M356
 
6.6%
d356
 
6.6%
r356
 
6.6%
a356
 
6.6%
Other values (3)1060
19.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
545 
False
543 
ValueCountFrequency (%)
True545
50.1%
False543
49.9%
2026-02-14T21:51:31.461881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Waist-to-Height Ratio
Real number (ℝ)

High correlation 

Distinct379
Distinct (%)34.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.52562689
Minimum0.259
Maximum0.804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.500697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.259
5-th percentile0.403
Q10.4565
median0.522
Q30.584
95-th percentile0.6667
Maximum0.804
Range0.545
Interquartile range (IQR)0.1275

Descriptive statistics

Standard deviation0.085524668
Coefficient of variation (CV)0.16270984
Kurtosis-0.03429148
Mean0.52562689
Median Absolute Deviation (MAD)0.063
Skewness0.33596539
Sum571.35643
Variance0.0073144689
MonotonicityNot monotonic
2026-02-14T21:51:31.551132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.42311
 
1.0%
0.4999
 
0.8%
0.4348
 
0.7%
0.5228
 
0.7%
0.5188
 
0.7%
0.5618
 
0.7%
0.4668
 
0.7%
0.5048
 
0.7%
0.4758
 
0.7%
0.5528
 
0.7%
Other values (369)1003
92.2%
ValueCountFrequency (%)
0.2591
0.1%
0.261
0.1%
0.2671
0.1%
0.2781
0.1%
0.361
0.1%
0.3621
0.1%
0.3651
0.1%
0.3661
0.1%
0.371
0.1%
0.3762
0.2%
ValueCountFrequency (%)
0.8042
0.2%
0.7872
0.2%
0.7851
0.1%
0.7841
0.1%
0.7831
0.1%
0.7821
0.1%
0.781
0.1%
0.7591
0.1%
0.7551
0.1%
0.7491
0.1%

Systolic BP
Real number (ℝ)

Missing 

Distinct98
Distinct (%)9.4%
Missing40
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean125.94618
Minimum49.914
Maximum202.711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.599839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.914
5-th percentile93
Q1108
median125
Q3141
95-th percentile168
Maximum202.711
Range152.797
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.714814
Coefficient of variation (CV)0.18035333
Kurtosis-0.14725468
Mean125.94618
Median Absolute Deviation (MAD)17
Skewness0.30049634
Sum131991.6
Variance515.96276
MonotonicityNot monotonic
2026-02-14T21:51:31.652462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13925
 
2.3%
10223
 
2.1%
11122
 
2.0%
12520
 
1.8%
13020
 
1.8%
11320
 
1.8%
14420
 
1.8%
13619
 
1.7%
12719
 
1.7%
12618
 
1.7%
Other values (88)842
77.4%
(Missing)40
 
3.7%
ValueCountFrequency (%)
49.9141
 
0.1%
51.1481
 
0.1%
53.2381
 
0.1%
58.5131
 
0.1%
9010
0.9%
9115
1.4%
9215
1.4%
9314
1.3%
9412
1.1%
9516
1.5%
ValueCountFrequency (%)
202.7111
 
0.1%
197.4991
 
0.1%
194.1731
 
0.1%
192.4011
 
0.1%
1794
0.4%
1787
0.6%
1775
0.5%
1763
0.3%
1754
0.4%
1744
0.4%

Diastolic BP
Real number (ℝ)

Missing 

Distinct72
Distinct (%)6.9%
Missing51
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean82.989943
Minimum34.047
Maximum134.066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.702219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.047
5-th percentile61
Q171
median82
Q394
95-th percentile113
Maximum134.066
Range100.019
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.714504
Coefficient of variation (CV)0.18935432
Kurtosis0.057954622
Mean82.989943
Median Absolute Deviation (MAD)11
Skewness0.33586535
Sum86060.571
Variance246.94565
MonotonicityNot monotonic
2026-02-14T21:51:31.746843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9130
 
2.8%
9228
 
2.6%
6528
 
2.6%
6127
 
2.5%
8426
 
2.4%
6326
 
2.4%
7126
 
2.4%
7826
 
2.4%
9526
 
2.4%
6626
 
2.4%
Other values (62)768
70.6%
(Missing)51
 
4.7%
ValueCountFrequency (%)
34.0471
 
0.1%
35.2432
 
0.2%
35.3171
 
0.1%
35.7931
 
0.1%
36.9951
 
0.1%
37.6021
 
0.1%
6022
2.0%
6127
2.5%
6216
1.5%
6326
2.4%
ValueCountFrequency (%)
134.0661
 
0.1%
133.8311
 
0.1%
132.1422
 
0.2%
131.561
 
0.1%
131.4251
 
0.1%
128.1651
 
0.1%
1197
0.6%
1187
0.6%
1174
0.4%
1164
0.4%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
Hypertension Stage 2
452 
Hypertension Stage 1
361 
Normal
200 
Elevated
75 

Length

Max length20
Median length20
Mean length16.599265
Min length6

Characters and Unicode

Total characters18.060
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowHypertension Stage 1
4th rowElevated
5th rowNormal

Common Values

ValueCountFrequency (%)
Hypertension Stage 2452
41.5%
Hypertension Stage 1361
33.2%
Normal200
18.4%
Elevated75
 
6.9%

Length

2026-02-14T21:51:31.788537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T21:51:31.814872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hypertension813
30.0%
stage813
30.0%
2452
16.7%
1361
13.3%
normal200
 
7.4%
elevated75
 
2.8%

Most occurring characters

ValueCountFrequency (%)
e2589
14.3%
t1701
 
9.4%
1626
 
9.0%
n1626
 
9.0%
a1088
 
6.0%
r1013
 
5.6%
o1013
 
5.6%
p813
 
4.5%
H813
 
4.5%
y813
 
4.5%
Other values (12)4965
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)18060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2589
14.3%
t1701
 
9.4%
1626
 
9.0%
n1626
 
9.0%
a1088
 
6.0%
r1013
 
5.6%
o1013
 
5.6%
p813
 
4.5%
H813
 
4.5%
y813
 
4.5%
Other values (12)4965
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2589
14.3%
t1701
 
9.4%
1626
 
9.0%
n1626
 
9.0%
a1088
 
6.0%
r1013
 
5.6%
o1013
 
5.6%
p813
 
4.5%
H813
 
4.5%
y813
 
4.5%
Other values (12)4965
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2589
14.3%
t1701
 
9.4%
1626
 
9.0%
n1626
 
9.0%
a1088
 
6.0%
r1013
 
5.6%
o1013
 
5.6%
p813
 
4.5%
H813
 
4.5%
y813
 
4.5%
Other values (12)4965
27.5%

Estimated LDL (mg/dL)
Real number (ℝ)

High correlation 

Distinct231
Distinct (%)21.3%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean114.4482
Minimum1
Maximum316.071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.853816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q164
median112
Q3162.75
95-th percentile207.95
Maximum316.071
Range315.071
Interquartile range (IQR)98.75

Descriptive statistics

Standard deviation59.640611
Coefficient of variation (CV)0.52111445
Kurtosis-0.57653659
Mean114.4482
Median Absolute Deviation (MAD)49
Skewness0.26694312
Sum123832.95
Variance3557.0025
MonotonicityNot monotonic
2026-02-14T21:51:31.903036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9013
 
1.2%
12412
 
1.1%
12512
 
1.1%
17812
 
1.1%
5811
 
1.0%
18811
 
1.0%
15111
 
1.0%
3911
 
1.0%
4311
 
1.0%
10310
 
0.9%
Other values (221)968
89.0%
ValueCountFrequency (%)
14
0.4%
62
0.2%
71
 
0.1%
81
 
0.1%
94
0.4%
102
0.2%
111
 
0.1%
123
0.3%
131
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
316.0711
0.1%
311.2461
0.1%
308.5141
0.1%
306.9211
0.1%
300.2272
0.2%
298.4921
0.1%
292.2551
0.1%
2372
0.2%
2341
0.1%
2321
0.1%

CVD Risk Score
Real number (ℝ)

High correlation 

Distinct848
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.527355
Minimum10.53
Maximum114.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-14T21:51:31.949400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.53
5-th percentile13.26
Q115.25
median16.97
Q318.8725
95-th percentile21.6802
Maximum114.98
Range104.45
Interquartile range (IQR)3.6225

Descriptive statistics

Standard deviation11.325999
Coefficient of variation (CV)0.61131228
Kurtosis49.950285
Mean18.527355
Median Absolute Deviation (MAD)1.816
Skewness6.9265409
Sum20157.762
Variance128.27826
MonotonicityNot monotonic
2026-02-14T21:51:31.994736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.454
 
0.4%
17.74
 
0.4%
14.164
 
0.4%
17.954
 
0.4%
16.854
 
0.4%
17.054
 
0.4%
144
 
0.4%
15.414
 
0.4%
16.334
 
0.4%
17.174
 
0.4%
Other values (838)1048
96.3%
ValueCountFrequency (%)
10.531
0.1%
10.861
0.1%
10.891
0.1%
11.111
0.1%
11.252
0.2%
11.31
0.1%
11.61
0.1%
11.611
0.1%
11.6331
0.1%
11.741
0.1%
ValueCountFrequency (%)
114.981
0.1%
114.9681
0.1%
114.1431
0.1%
112.3431
0.1%
111.0081
0.1%
110.0941
0.1%
104.2711
0.1%
104.0871
0.1%
104.0021
0.1%
99.7751
0.1%

CVD Risk Level
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
HIGH
516 
INTERMEDIARY
417 
LOW
155 

Length

Max length12
Median length4
Mean length6.9237132
Min length3

Characters and Unicode

Total characters7.533
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIGH
2nd rowINTERMEDIARY
3rd rowHIGH
4th rowHIGH
5th rowINTERMEDIARY

Common Values

ValueCountFrequency (%)
HIGH516
47.4%
INTERMEDIARY417
38.3%
LOW155
 
14.2%

Length

2026-02-14T21:51:32.036553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-14T21:51:32.062378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high516
47.4%
intermediary417
38.3%
low155
 
14.2%

Most occurring characters

ValueCountFrequency (%)
I1350
17.9%
H1032
13.7%
E834
11.1%
R834
11.1%
G516
 
6.8%
N417
 
5.5%
T417
 
5.5%
M417
 
5.5%
D417
 
5.5%
A417
 
5.5%
Other values (4)882
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)7533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I1350
17.9%
H1032
13.7%
E834
11.1%
R834
11.1%
G516
 
6.8%
N417
 
5.5%
T417
 
5.5%
M417
 
5.5%
D417
 
5.5%
A417
 
5.5%
Other values (4)882
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I1350
17.9%
H1032
13.7%
E834
11.1%
R834
11.1%
G516
 
6.8%
N417
 
5.5%
T417
 
5.5%
M417
 
5.5%
D417
 
5.5%
A417
 
5.5%
Other values (4)882
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I1350
17.9%
H1032
13.7%
E834
11.1%
R834
11.1%
G516
 
6.8%
N417
 
5.5%
T417
 
5.5%
M417
 
5.5%
D417
 
5.5%
A417
 
5.5%
Other values (4)882
11.7%

Interactions

2026-02-14T21:51:28.963313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.617973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.109953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.623903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.164615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.803188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.314329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.860857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.372190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.897780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.434281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.953856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.465562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.995732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.662392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.144038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.659467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.196365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.844051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.352119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.895140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.409326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.935601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.468849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.993552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.500294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.033233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.704428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.182882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.702527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.237207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.891172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.397879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.936805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.449541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.979639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.517905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.034280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.539272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.070690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.741600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.226383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.742618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.271616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.930021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.439476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.980731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.494526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.023056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.558513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.072048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.578291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.105246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.774997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.264457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.787902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.303710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.965357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.477478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.018514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.531935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.058141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.595807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.113732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.614201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.144888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.810482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.301630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.830037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.338339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.999644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.517172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.054117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.571563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.093747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.634010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.149438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.651095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.200249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.851039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.343336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.873228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.375690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.039088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.559920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.094656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.620300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.141257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.680442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.198172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.695418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.236120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.892572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.379531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.911295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.410392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.074808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.600850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.142772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.657584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.197303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.717819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.238968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.731335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.274812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.931551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.425801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.960459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.448356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.119137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.647878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.184658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.698308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.237506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.757558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.278752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.770831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.313383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:22.972391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.468673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.001990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.663195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.167019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.688663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.220631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.737387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.273784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.795552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.316112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.809161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.355537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.007470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.508396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.047556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.700028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.205827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.732567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.258444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.777365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.311764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.834579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.356212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.849257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.394168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.041036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.544994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.085322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.733394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.242534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.775923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.294508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.815349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.351213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.873682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.389283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.884771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:29.440638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.077656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:23.586393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.125246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:24.768647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.278500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:25.816304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.332847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:26.854574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.392439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:27.917313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.432004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-14T21:51:28.926446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-14T21:51:32.100715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Abdominal Circumference (cm)AgeBMIBlood Pressure CategoryCVD Risk LevelCVD Risk ScoreDiabetes StatusDiastolic BPEstimated LDL (mg/dL)Family History of CVDFasting Blood Sugar (mg/dL)HDL (mg/dL)Height (m)Physical Activity LevelSexSmoking StatusSystolic BPTotal Cholesterol (mg/dL)Waist-to-Height RatioWeight (kg)
Abdominal Circumference (cm)1.0000.0640.0280.0780.0590.0960.0970.0380.0550.0000.008-0.028-0.0100.0470.0000.0360.0430.0490.8870.050
Age0.0641.0000.0270.0820.1540.0590.0850.046-0.0000.0000.0900.0340.0330.0000.0000.0450.0630.0110.0380.010
BMI0.0280.0271.0000.0570.1430.6010.0260.0490.0180.0000.043-0.003-0.1660.0000.0000.0000.0100.0210.0880.647
Blood Pressure Category0.0780.0820.0571.0000.0810.0570.0000.4190.0000.0000.0820.0280.0670.0460.0000.0000.4500.0000.0230.000
CVD Risk Level0.0590.1540.1430.0811.0000.0470.1730.1040.1490.2230.1180.1330.1350.1290.0000.2160.1180.1290.0310.109
CVD Risk Score0.0960.0590.6010.0570.0471.0000.1810.1070.4320.0540.0780.045-0.0710.0180.0000.0000.4130.4470.1180.405
Diabetes Status0.0970.0850.0260.0000.1730.1811.0000.0000.0370.0000.0000.0000.0260.0520.0000.0000.0470.0270.0190.086
Diastolic BP0.0380.0460.0490.4190.1040.1070.0001.0000.1180.0000.0730.0100.0060.0000.0190.0000.0250.1290.0300.040
Estimated LDL (mg/dL)0.055-0.0000.0180.0000.1490.4320.0370.1181.0000.0210.003-0.1480.0320.0000.0290.0000.0200.9310.034-0.003
Family History of CVD0.0000.0000.0000.0000.2230.0540.0000.0000.0211.0000.0440.0270.0790.0150.0240.0000.0000.0230.0000.000
Fasting Blood Sugar (mg/dL)0.0080.0900.0430.0820.1180.0780.0000.0730.0030.0441.0000.0650.0270.0000.0490.0000.0610.006-0.0160.039
HDL (mg/dL)-0.0280.034-0.0030.0280.1330.0450.0000.010-0.1480.0270.0651.000-0.0160.0000.0460.0000.0620.112-0.0160.013
Height (m)-0.0100.033-0.1660.0670.135-0.0710.0260.0060.0320.0790.027-0.0161.0000.0000.0170.0340.0230.025-0.3620.052
Physical Activity Level0.0470.0000.0000.0460.1290.0180.0520.0000.0000.0150.0000.0000.0001.0000.0000.0000.0000.0430.0000.000
Sex0.0000.0000.0000.0000.0000.0000.0000.0190.0290.0240.0490.0460.0170.0001.0000.0350.0000.0000.0000.042
Smoking Status0.0360.0450.0000.0000.2160.0000.0000.0000.0000.0000.0000.0000.0340.0000.0351.0000.0000.0000.0000.000
Systolic BP0.0430.0630.0100.4500.1180.4130.0470.0250.0200.0000.0610.0620.0230.0000.0000.0001.0000.0250.024-0.003
Total Cholesterol (mg/dL)0.0490.0110.0210.0000.1290.4470.0270.1290.9310.0230.0060.1120.0250.0430.0000.0000.0251.0000.0330.004
Waist-to-Height Ratio0.8870.0380.0880.0230.0310.1180.0190.0300.0340.000-0.016-0.016-0.3620.0000.0000.0000.0240.0331.0000.035
Weight (kg)0.0500.0100.6470.0000.1090.4050.0860.040-0.0030.0000.0390.0130.0520.0000.0420.000-0.0030.0040.0351.000

Missing values

2026-02-14T21:51:29.512345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-14T21:51:29.592577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-14T21:51:29.694920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDWaist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
0isDx5313November 08, 2023M44.0114.30001.72038.600100.000112/83228.077.091.0YYHighN0.581112.083.0Hypertension Stage 1121.019.880HIGH
1LHCK296120/03/2024F57.092.92301.84233.116106.315101/91158.071.076.0NYHighY0.577101.091.0Hypertension Stage 257.016.833INTERMEDIARY
2dCDO1109April 18, 2022F35.0113.30001.78035.80079.60092/89158.034.0111.0YNModerateY0.44792.089.0Hypertension Stage 194.014.920HIGH
3pnpE108001/11/2024F48.0102.20001.75033.400106.700121/68207.049.0147.0YYLowY0.610121.068.0Elevated128.018.870HIGH
4MQyB274725 Mar 24M43.052.70001.85015.400107.700107/61105.032.070.0YNHighN0.582107.061.0Normal43.010.530INTERMEDIARY
5DHdn896822 May 25F31.087.00001.66031.60091.500139/81207.056.082.0NNLowY0.551139.081.0Hypertension Stage 1121.017.410HIGH
6vkQL9700October 26, 2023M69.059.68401.94023.914117.986106/115206.042.0140.0YYHighY0.608106.0115.0Hypertension Stage 2134.016.203HIGH
7nktq6689January 16, 2022F57.0100.13001.84022.24280.814165/99123.054.094.0NNLowN0.439165.099.0Hypertension Stage 239.015.158LOW
8SMmI395610/11/2023M43.0117.90001.90032.70093.100127/99293.069.0125.0NNModerateY0.490127.099.0Hypertension Stage 2194.018.750INTERMEDIARY
9aLYL918805 Dec 21F58.097.38751.75031.80071.400135/88272.058.0132.0NNLowN0.408135.088.0Hypertension Stage 1184.018.550HIGH
Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDWaist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
1218wWyM613910 Mar 22F57.080.0000001.61030.90093.0000112/71296.067.000101.0YNLowN0.578112.071.0Normal199.017.700HIGH
1220wwQq629006 Aug 24M58.0118.3000001.66042.900103.2520119/65149.066.000127.0NYModerateN0.622119.065.0Normal53.093.898INTERMEDIARY
1222xFVW5778June 09, 2021M31.051.3409621.58020.56686.8760142/74213.076.000182.0YNHighN0.550142.074.0Hypertension Stage 2107.015.473HIGH
1224xbYu992920/11/2024M46.072.0000001.75023.50089.1000100/90198.076.000141.0NNLowN0.509100.090.0Hypertension Stage 292.013.660INTERMEDIARY
1226yAIB346829 Jan 22F53.070.3000001.6706.23588.8000130/97277.060.000113.0NNLowY0.532130.097.0Hypertension Stage 1187.017.080HIGH
1228yvsn300528 Oct 20F60.054.3000001.81016.60099.1000133/65187.078.00080.0NYHighN0.548133.065.0Hypertension Stage 179.015.710INTERMEDIARY
1230zZle545513 Apr 22M53.0108.1590001.92524.08179.1175162/94141.00.612178.0YYModerateN0.411162.094.0Hypertension Stage 263.017.736HIGH
1232zcgB304812/06/2020M38.060.4360001.50620.26979.7000168/63119.046.000190.0NNHighN0.529168.063.0Hypertension Stage 243.014.834HIGH
1234zhZi8857December 05, 2025F36.081.2000001.75026.50074.0000141/76225.031.000124.0YNModerateY0.423141.0NaNHypertension Stage 2164.016.850HIGH
1236zxhX5525November 13, 2021M26.058.9530001.68825.28676.8040110/114258.058.000146.0NNHighY0.455110.0114.0Hypertension Stage 2170.015.717HIGH